A New Selective Ensemble Approach for Data Streams Classification

V. Grossi and F. Turini (Italy)

Keywords

Mining data streams, classiﬁcation, knowledge discovery

Abstract

Mining data streams has recently become an important and
challenging task for a wide range of applications, including
sensor networks and web applications. The massive quantity of streaming data coupled with concept drifting are two
crucial issues in mining data streams. This paper proposes
a selective ensemble approach for data streams classiﬁcation, introducing two distinct structures to face the problem
of data management and mining. On the one hand, our ap
proach provides a synthetic structure which maximizes data
availability, guaranteeing a single data access. On the other,
given the synthetic structure, a selective ensemble of classiﬁers is managed through time to provide a good prediction
accuracy. Both components are designed to maximize data
usage and accuracy even in the presence of concept drifting, providing a good trade-off between data access man
agement and the quality of the model.